Townhall⁠92%

Wait, Ketanji Brown Jackson Really Said That…on This Podcast? ⁠95%

By Matt Vespa⁠88%

7/31/2026, 12:00:00 AM

BS Summary: This article contains 25 faulty reasoning types, including Politically Right Leaning Bias, Negativity Bias, and Out-Group Homogeneity Bias, with Biased Writer Voice as the most egregious example at 78% saturation with 170 hits. Analysis detected 1,011 faulty-reasoning hits from 218 analyzed words, generating a BS Score of 88.9% and a BS Rank of ⁠95% (1,376 of 26,881 articles). This article is worse (more manipulative) than 94.90% of the article peer group.

There’s not much to add here, except if you thought Supreme Court Justice Ketanji Brown Jackson was out of her depth with her opinions, you’re in for a treat with this clip. 
Jackson has been a source of comedy and concern: the conservative side is losing patience with her inability to understand the law, and that frustration has also reached the court’s liberal side. 
Some don’t even join her dissents; they file separate ones. 
Justice Jackson said she’s very careful about which public engagements she participates in to avoid appearing partisan. 
It’s a classic lack of self-awareness that’s frankly remarkable. 
She wants to seem impartial, which is fine, but she made these remarks on Michelle Obama’s podcast. 
You cannot make this up: 
Like, you can’t be that dense, right? 
But we’re talking about the American liberal, and even worse, someone from their supposed academic circles, where nuance is also dead. 
Justice Jackson just doesn’t understand her role, so it’s not shocking that she self-immolated here. 
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Article reasoning-pattern comparisonThis article: 0.0%Matt Vespa: 6.0%Townhall: 5.0%Confirmation Bias0.0%This article: 0.0%Matt Vespa: 0.7%Townhall: 0.7%Anchoring Bias0.0%This article: 0.0%Matt Vespa: 2.6%Townhall: 2.6%Availability Heuristic0.0%This article: 9.6%Matt Vespa: 0.8%Townhall: 0.8%Representativeness Heuristic9.6%This article: 0.0%Matt Vespa: 1.1%Townhall: 0.7%Hindsight Bias0.0%This article: 0.0%Matt Vespa: 0.9%Townhall: 1.6%Overconfidence Bias0.0%This article: 4.1%Matt Vespa: 7.7%Townhall: 10.0%Framing Effect4.1%This article: 6.4%Matt Vespa: 0.4%Townhall: 0.4%Loss Aversion6.4%This article: 0.0%Matt Vespa: 0.2%Townhall: 0.3%Status Quo Bias0.0%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.1%Sunk Cost Effect0.0%This article: 0.0%Matt Vespa: 0.8%Townhall: 1.3%Optimism Bias0.0%This article: 14.7%Matt Vespa: 1.9%Townhall: 1.7%Pessimism Bias14.7%This article: 64.2%Matt Vespa: 15.2%Townhall: 12.9%Negativity Bias64.2%This article: 0.0%Matt Vespa: 1.0%Townhall: 1.6%Self-Serving Bias0.0%This article: 22.5%Matt Vespa: 1.8%Townhall: 1.8%Fundamental Attribution Error22.5%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.1%Actor-Observer Bias0.0%This article: 14.7%Matt Vespa: 4.4%Townhall: 4.3%In-Group Bias14.7%This article: 24.3%Matt Vespa: 3.6%Townhall: 3.0%Out-Group Homogeneity Bias24.3%This article: 0.0%Matt Vespa: 1.2%Townhall: 1.5%Halo Effect0.0%This article: 0.0%Matt Vespa: 1.8%Townhall: 0.9%Horn Effect0.0%This article: 0.0%Matt Vespa: 0.0%Townhall: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Matt Vespa: 0.9%Townhall: 1.2%Recency Bias0.0%This article: 0.0%Matt Vespa: 0.7%Townhall: 0.5%Primacy Effect0.0%This article: 0.0%Matt Vespa: 0.0%Townhall: 0.0%Blind-Spot Bias0.0%This article: 22.0%Matt Vespa: 6.7%Townhall: 5.0%Ad Hominem22.0%This article: 0.0%Matt Vespa: 1.1%Townhall: 1.9%Straw Man0.0%This article: 7.8%Matt Vespa: 3.5%Townhall: 3.9%Appeal to Authority7.8%This article: 0.0%Matt Vespa: 1.8%Townhall: 1.8%False Dilemma0.0%This article: 6.9%Matt Vespa: 1.8%Townhall: 1.5%Slippery Slope6.9%This article: 0.0%Matt Vespa: 0.2%Townhall: 0.1%Circular Reasoning0.0%This article: 24.3%Matt Vespa: 10.5%Townhall: 9.0%Hasty Generalization24.3%This article: 7.8%Matt Vespa: 0.6%Townhall: 0.6%Red Herring7.8%This article: 0.0%Matt Vespa: 1.3%Townhall: 1.2%Bandwagon0.0%This article: 15.6%Matt Vespa: 8.7%Townhall: 10.3%Appeal to Emotion15.6%This article: 0.0%Matt Vespa: 2.1%Townhall: 2.0%Begging the Question0.0%This article: 6.9%Matt Vespa: 2.0%Townhall: 2.2%Post Hoc (False Cause)6.9%This article: 0.0%Matt Vespa: 0.3%Townhall: 0.6%Tu Quoque0.0%This article: 0.0%Matt Vespa: 1.9%Townhall: 1.4%Burden of Proof0.0%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.1%Appeal to Nature0.0%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.1%Composition/Division0.0%This article: 4.6%Matt Vespa: 2.9%Townhall: 1.5%Anecdotal4.6%This article: 9.6%Matt Vespa: 0.2%Townhall: 0.1%No True Scotsman9.6%This article: 7.8%Matt Vespa: 1.4%Townhall: 1.8%Ambiguity (Equivocation)7.8%This article: 0.0%Matt Vespa: 0.0%Townhall: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.0%Middle Ground0.0%This article: 6.4%Matt Vespa: 0.1%Townhall: 0.1%Personal Incredulity6.4%This article: 0.0%Matt Vespa: 0.1%Townhall: 0.2%Special Pleading0.0%This article: 0.0%Matt Vespa: 0.4%Townhall: 0.5%Genetic Fallacy0.0%This article: 7.8%Matt Vespa: 3.1%Townhall: 3.0%Unattributed Quote7.8%This article: 4.1%Matt Vespa: 2.3%Townhall: 2.6%Quote-first Misdirection4.1%This article: 78.0%Matt Vespa: 17.9%Townhall: 15.2%Biased Writer Voice78.0%This article: 7.8%Matt Vespa: 3.6%Townhall: 4.9%Indoctrination7.8%This article: 0.0%Matt Vespa: 0.5%Townhall: 0.2%Politically Left Leaning Bias0.0%This article: 65.6%Matt Vespa: 12.9%Townhall: 13.3%Politically Right Leaning Bias65.6%This article: 20.2%Matt Vespa: 5.2%Townhall: 5.0%Attempt to Sell a Product or S…20.2%

218 words analyzed.

Speakers

2speakers20%attributed speech174writer words
Selected voice

Townhall

100%flagged-word coverage
30 attributed words68% of attributed speech100% writer coverage
0%50.0%100.0%Attempt to Sell a Product +100.0 ptsWriter: 0.0%Townhall: 100.0%100.0%Biased Writer Voice-46.9 ptsWriter: 90.2%Townhall: 43.3%43.3%Politically Right Leaning -15.7 ptsWriter: 72.4%Townhall: 56.7%56.7%Indoctrination+56.7 ptsWriter: 0.0%Townhall: 56.7%56.7%Unattributed Quote-9.8 ptsWriter: 9.8%Townhall: 0.0%0.0%Quote-first Misdirection-5.2 ptsWriter: 5.2%Townhall: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.